Resumen:

Business process improvement can drastically influence in the profit of corporations and helps them to remain viable in a slowdown economy such as the present one. For companies to remain viable in the face of intense global competition, they must be able to cBusiness process improvement can drastically influence in the profit of corporations and helps them to remain viable in a slowdown economy such as the present one. For companies to remain viable in the face of intense global competition, they must be able to continuously improve their processes in order to respond rapidly to changing business environments. In this regard, the analysis of data plays an important role for improving and optimizing the enterprise performance, and this is essential for running a competitive business and reacting quickly in response to competitors. However, this is a challenging task that requires complex and robust supporting systems. Traditional Business Intelligence (BI) platforms and decision support systems have become key tools for business users in decision making. While traditionally used to discover trends and relationships in large, complex business data sets, these systems are not sufficient to meet today's business needs. There currently exists an increasing demand for more advanced analytics such as root cause analysis of performance issues, predictive analysis and the ability to perform "what-if" type simulations. These features are powerful assets to analysts for expanding their knowledge beyond the limits of what current platforms typically can offer. Furthermore, these platforms are normally business domain specific and have not been sufficiently process-aware to support the needs of process improvement type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs in a timely manner, with no structure, and produced on a variety of heterogeneous environments. This doctoral thesis aims to put real business process improvement (BPI) technology in hands of business users with the aim at improving the performance of their business processes. It addresses the research challenges abovementioned by devising a cloud-based solution that supports business process intelligence activities on highly distributed environments. Likewise, it proposes an implementation methodology for assisting analysts in sustaining a comprehensive process improvement program, whose activities are hard to undertake without the use of effective supporting systems[+][-]